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Skip to main content. Log In Sign Up. Sagar PhD. According to the British Council, a massive increase in the number of people learning English has already begun all over the world, and is likely to reach a peak of around 2 billion in the next 10—15 years. Learner motivations are mainly professional, whether as future employees or entrepreneurs wanting to develop business opportunities in neighbouring regions or on the global stage. In parallel, smart phones and tablets are being purchased massively. According to the Unesco, there are presently almost 6 billion mobile phone subscriptions in the world, and Internet is accessed three times more via mobile devices than via computers.

Latest academic research results on mobile learning applied to language learning also sheds a light on strategies to follow when designing a language mobile learning project.

This research is reviewed, and conclusions are drawn on the barriers and challenges that affect the deployment of projects as well as guidelines and future lines of research for promoting mobile learning projects of English.

Varieties of English are being practised and used between non-native speakers in different regions. Native English-speakers are no longer necessarily the most desirable teachers, nor is classroom learning necessarily the only or best solution to reach such a great number of adult learners. The skill of English not only increases work and business opportunities, it is in fact becoming a basic skill, and in some countries closer to a Second language or a Lingua Franca, than to a foreign language.

Because of this, it is urgent to avoid the lack of knowledge of English becoming an additional factor of social exclusion and worsening divide between those who speak the language and those who do not.

From the combination of theory and observed practise, we draw conclusions on the design and questions for future projects. After describing how the programme works and the services it provides, we will describe more precisely how it centres on learners and adapts its services to their needs.

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Features proper to ML that we believe can be reused in other programmes of its kind are listed in section 3: Most of the information on the programme referred to in this article was kindly provided by Media Action.

It was launched in and aims to help a quarter of the adult population of the country improve their English by 25 million people.

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It is a project for mobile learning in the broad sense, as it offers English learning resources via mobile, TV, newspapers, website, CDs and peer-to peer clubs. On one hand, users can use their mobile phones to access English language lessons. These lessons cover pronunciation, vocabulary, specific language fields such as business , and grammar, using Interactive Voice Response IVR. Users select their level, and then choose to either follow an audio lesson or take a quiz on their handset.

Lessons and quizzes can also be accessed through a website which offers interactive, online learning as well as educational downloads. TV complements the lessons and quizzes.

The other, a weekly Drama that ran for 24 episodes Bishaash , followed by English language back-up. The second series of this show encourages the audience to learn English through quizzes, comedy sketches and short dramas using the target language from the syllabus. Due to ongoing monitoring of user reactions to the service, it is planned that in , a new TV series focusing on absolute beginners be broadcast in March and a network of peer-led English learning clubs across Bangladesh is also being set up in partnership with local NGOs.

As much as 7. That is almost one third of the target, only two years into the project, with four more years to go. Adaptation to the user has been central in the approach, before the design of the project via a survey on cable and satellite viewers and mobile phone users and during the provision of the service. The result of this research is the adaptation of the service in all its aspects: Fig 1.

We will now overview current state of research in mobile learning applied to language learning. From the technological viewpoint, ML could be described as: Thus, mobile learning implies different dimensions to mobility: Another important branch of research concerns technical adaptations or innovations for enabling MALL Mobile Assisted Language Learning and mobile learning in general.

An encompassing mobile learning theory has not yet been articulated, which is unsurprising given the novel and complex nature of the phenomenon. However, Sharples et al brings us closer to what a ML theory could be, by setting five criteria that a ML theory should take into account: This table aims to structure in a coherent way different results of mobile learning research, and how they can relate to each other, in order to develop a general understanding of ML and its components for the purposes set-out in this article.

Moments of interest are Seamless learning [11]. Assisted Language learning[12]. Versatile learning Explicit use scenarios and design of experiences [8]. Combination of Formal and Informal Learning Lifelong learning [15] Informal and lifelong learning Highly portable, individual, theories unobtrusive, adaptable to context, available anywhere, persistent allowing accumulation , useful, easy to use [20] Informal Learning [29] Informal, non formal and formal learning theories [29] Learning as a Constructive and Social Process Exploration and Tool-mediated socio-cultural Functions for Interactions with peers conversation across activity.

Design of activity according to device Available technology influences learning [14] choices. Such resources for learning language are highly flexible in their usage content, moment of use, re-use , and can be personalised. They are designed to adapt to learner daily schedules, tastes, and learning activities are adapted to type of device used, based partly on Technology Acceptance Models.

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Moments of interest during the day are transformed into language learning moments via adapted resources that assist your own knowledge and skill practise, for example via augmented reality tools. Though knowledge building of English is organic, some order is introduced in the learning. The learning path is based on a combination of formal and informal learning, and not according to external time limitations or schedules. Resources for learning the language are independent of space you can find them in different platforms, almost anywhere, they stay with you , or time available at any moment.

Learned pool of knowledge or production grows cumulatively, and is saved like an ongoing and dynamic portfolio. If all these elements of learning were effectively brought together this way, research would have to be carried out on defining the new cognitive, socio-cultural and constructivist processes by which language learning occurs as opposed to traditional classroom learning which is the setting that underpins established theories on language learning.

As a mere remark, these processes may share some similarity with processes of learning found in immersion, especially if English is considered as a Second Language in the region where the mobile learning is taking place. The BBC Janala service does not put into practise all the components of ML for language learning described previously.

However some key features from the table are being applied in the programme and we can learn from them. Many components have been materialised, notably: Via the Anmar Engreji course, learning paths, tracking and a general syllabus with a start and finish combine the desire for freedom of use with prompts to assist self-directed learning.

Measures proper to ELL are the adaptation to local culture of the content of the lessons references to local daily life , and the target language learned, i. The BBC Janala project does not seem to have focused on functions that would allow for a dynamic patterns of social interaction, or creative and collaborative functions for learning English, outside the setting-up of peer-to-peer clubs.

Situated activities were not included either. These functions require more advanced or expensive technology not applicable in this project. The programme is deliberately based on devices learners already possess and availability thanks to cheapness of service. We then describe challenges such projects can encounter. Local teachers an pedagogues know target learner characteristics and perceptions, media specialists can publicise the project, and enable the choice of platforms such as TV or newspapers to include English learning, national telecom operators allow to provide a consistent set of functionality cross-operator, at a unique and accessible tariff, the government defines English language learning policy, implications and financing.

A detailed summary of this research is given at the beginning of this article see Fig.

BBC Janala English Learning Book-1

English as a Second Language recognises the society where it is taught. The ELT paradigm needs to be adapted to the target English skills, as done in the BBC Janala project, where course content and target language were localised to Bangladesh. BBC Janala offered mobility via multiplatform lesson delivery, adaptation of learning content to platform, tracking of progress cross-platform; small-sized and uncomplicated lessons doable in spare moments, anywhere , bookmark functions, and repetition options.

This syllabus should be cross-platform; it should include tracking of progress functionality for the user; a beginning and end to the course and a reward system completion certificate for example.

Design studies to carry out during the programme to test improvements in English language speaking skills Additional guidelines we can deduce from research on ML for setting up ML projects for ELL are as follow: Problem based learning, case-based learning or context-aware learning.

These could be developed thanks to frameworks that combine individual, collaborative and ubiquitous learning to model the way in which students develop and refine their linguistic competences Read, T.

Indeed, one barrier to this is lack of development of innovative ELL mobile technology and applications. We will conclude this article with a reflection on the barriers to achieving a ML for ELL project today and some questions to answer in the future. This means having to manage and create cross-platform resources, without knowing their success or outcome, as this depends on learner behaviours.

As mobile-learning is learner-centred, standardized processes are not sufficient.

What is more, as related above, a ML project involves a large amount of actors from different backgrounds, and the coordination of these partners is an additional difficulty. Other challenges relate to the nature of language learning.

On one hand, a theory and clear paradigm to follow in the context of mobile learning for ELL has not been developed sufficiently, and nor have related applications, learning objects or resources though specialised research into such tools is going on.

Language Learning is a demanding subject, as it involves mastering four different skills oral and written comprehension, speaking and writing , it is not a simple pool of knowledge. Acquiring such skills involves frequent practise, which makes mobile learning all the more adapted to language learning as it can be applied daily, but does pose extra demands on functionality for practise. Lastly, materials and tools for learning English should more often than not be localised English variety, etc.

Furthermore, mobile learning tools are still expensive to develop. In most countries, it is still difficult to find widespread ownership of tablets and smart-phones that would allow for a variety and flexibility of innovative learning objects to be used. This problem may slowly recede with cheaper prices, connection tariffs and further innovations. WordPress Shortcode. Monzurul Karim Shanchay Follow. Published in: Full Name Comment goes here. Are you sure you want to Yes No.

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